• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.8
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• pp.2881-2894
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• 2014

In complicated environment, context information plays an important role in image segmentation/labeling. The recently proposed auto-context algorithm is one of the effective context-based methods. However, the standard auto-context approach samples the context locations utilizing a fixed radius sequence, which is sensitive to large scale-change of objects. In this paper, we present a scale invariant auto-context (SIAC) algorithm which is an improved version of the auto-context algorithm. In order to achieve scale-invariance, we try to approximate the optimal scale for the image in an iterative way and adopt the corresponding optimal radius sequence for context location sampling, both in training and testing. In each iteration of the proposed SIAC algorithm, we use the current classification map to estimate the image scale, and the corresponding radius sequence is then used for choosing context locations. The algorithm iteratively updates the classification maps, as well as the image scales, until convergence. We demonstrate the SIAC algorithm on several image segmentation/labeling tasks. The results demonstrate improvement over the standard auto-context algorithm when large scale-change of objects exists.

• Journal of Korea Multimedia Society
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• v.22 no.11
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• pp.1251-1258
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• 2019

Intelligent tutoring system enables users to effectively learn by utilizing various artificial intelligence techniques. For instance, it can recommend a proper curriculum or learning method to individual users based on their learning history. To do this effectively, user's characteristics need to be analyzed and classified based on various aspects such as interest, learning ability, and personality. Even though data labeled by the characteristics are required for more accurate classification, it is not easy to acquire enough amount of labeled data due to the labeling cost. On the other hand, unlabeled data should not need labeling process to make a large number of unlabeled data be collected and utilized. In this paper, we propose a semi-supervised learning method based on feedback variational auto-encoder(FVAE), which uses both labeled data and unlabeled data. FVAE is a variation of variational auto-encoder(VAE), where a multi-layer perceptron is added for giving feedback. Using unlabeled data, we train FVAE and fetch the encoder of FVAE. And then, we extract features from labeled data by using the encoder and train classifiers with the extracted features. In the experiments, we proved that FVAE-based semi-supervised learning was superior to VAE-based method in terms with accuracy and F1 score.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.222-224
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• 2022

Deep learning-based computer vision applications in the field of object segmentation take a transfer learning method using hyperparameters and models pretrained and distributed by STOA techniques to improve performance. Custom datasets used in this process require a lot of resources, such as time and labeling, in labeling tasks to generate Ground Truth information. In this paper, we present an automatic labeling construction method using object segmentation techniques so that resources such as time and labeling can be used less to build custom datasets used in deep learning neural networks.

• Journal of IKEEE
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• v.26 no.3
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• pp.341-348
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• 2022

Wi-Fi Sensing, which uses Wi-Fi technology to sense the surrounding environments, has strong potentials in a variety of sensing applications. Recently several advanced deep learning-based solutions using CSI (Channel State Information) data have achieved high performance, but it is still difficult to use in practice without explicit data collection, which requires expensive adaptation efforts for model retraining. In this study, we propose a Channel State Information Automatic Labeling System (CALS) that automatically collects and labels training CSI data for deep learning-based Wi-Fi sensing systems. The proposed system allows the CSI data collection process to efficiently collect labeled CSI for labeling for supervised learning using computer vision technologies such as object detection algorithms. We built a prototype of CALS to demonstrate its efficiency and collected data to train deep learning models for detecting the presence of a person in an indoor environment, showing to achieve an accuracy of over 90% with the auto-labeled data sets generated by CALS.

• Journal of the Korea Society of Computer and Information
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• v.24 no.9
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• pp.21-27
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• 2019

In this paper, we propose a deep auto-encoder-based pipe leak detection (PLD) technique from time-series acoustic data collected by microphone sensor nodes. The key idea of the proposed technique is to learn representative features of the leak-free state using leak-free time-series acoustic data and the deep auto-encoder. The proposed technique can be used to create a PLD model that detects leaks in the pipeline in an unsupervised learning manner. This means that we only use leak-free data without labeling while training the deep auto-encoder. In addition, when compared to the previous supervised learning-based PLD method that uses image features, this technique does not require complex preprocessing of time-series acoustic data owing to the unsupervised feature extraction scheme. The experimental results show that the proposed PLD method using the deep auto-encoder can provide reliable PLD accuracy even considering unsupervised learning-based feature extraction.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.6
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• pp.585-591
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• 2015

In transcription for spoken language research, labeling is a work linking text-represented utterance to recorded speech. Most existing labeling tools have been working manually. Semi-automatic labeling we are proposing consists of automation module and manual adjustment module. Automation module extracts voice boundaries utilizing G.Saha's algorithm, and predicts utterance boundaries using the number and length of utterance which established utterance text. For maintaining existing manual tool's accuracy, we provide manual adjustment user interface revising the auto-labeling utterance boundaries. The implemented tool of our semi-automatic algorithm speed up to 27% than existing manual labeling tools.

• Nutrition Research and Practice
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• v.9 no.1
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• pp.92-98
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• 2015

BACKGROUND/OBJECTIVES: The prevalence of food allergies in Korean children aged 6 to 12 years increased from 10.9% in 1995 to 12.6% in 2012 according to nationwide population studies. Treatment for food allergies is avoidance of allergenic-related foods and epinephrine auto-injector (EPI) for accidental allergic reactions. This study compared knowledge and perception of food allergy labeling and dietary practices of students. SUBJECTS/METHODS: The study was conducted with the fourth to sixth grade students from an elementary school in Yongin. A total of 437 response rate (95%) questionnaires were collected and statistically analyzed. RESULTS: The prevalence of food allergy among respondents was 19.7%, and the most common food allergy-related symptoms were urticaria, followed by itching, vomiting and nausea. Food allergens, other than 12 statutory food allergens, included cheese, cucumber, kiwi, melon, clam, green tea, walnut, grape, apricot and pineapple. Children with and without food allergy experience had a similar level of knowledge on food allergies. Children with food allergy experience thought that food allergy-related labeling on school menus was not clear or informative. CONCLUSION: To understand food allergies and prevent allergic reactions to school foodservice among children, schools must provide more concrete and customized food allergy education.

• Journal of the Korean Institute of Landscape Architecture
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• v.23 no.4
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• pp.20-27
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• 1996

The main purpose of this research is to develop a program supporting landscape planting design on AutoCAD basis using AutoLISP and DCL language. Current CAD use in landscape architecture field is mainly focused on customizing plant symbols for supporting two dimensional drafting rather than three dimensional consideration. This program is composed of eight module a such as PLANT module for inserting plant symbols, LABEL module for labeling task, SIMULATION module for simulating plant growth and seasonal color variation, TABLE module for generating plant table automatically, BUILDING module, BLOCK module, UTILITY module for deleting, transforming, shading symbols and DB MANAGER module for manipulating data. Design automation ability using automatic object recognition technique in this program allows AutoCAD to be used as a design tool in addition to its main role as a drafting tool through supporting landscape designers to generate many alternatives in the early phase of design.

• Maxillofacial Plastic and Reconstructive Surgery
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• v.31 no.3
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• pp.207-215
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• 2009

Purpose: The aim of the present study is to evaluate the long term bone healing after horizontal ridge augmentation using auto block bone graft for implant installation timing. Materials and Methods: Five Beagle dogs(which were 14 months old and weighted approximately 10kg). In surgery 1(extraction & bone defect), premolars(P2, P3,P4) were extracted and the buccal bone plate was removed to create a horizontally defected ridge. After three months healing, in surgery 2(ridge augmentation). Auto block bone grafts from the mandibular ramus were used in filling the bone defects were fixed with stabilizing screws. The following fluorochrome labels were given intravenously to the beagle dogs: oxytetracycline 1week after the surgery, alizarin red 4 weeks after the surgery, calcein blue 8 weeks after the surgery. The tissue samples were obtained from the sacrificed dogs of 1, 4, 8, 12, 16 weeks after the surgery. Non-decalcified sections were prepared by resin embedding and microsection to find thickness of $10{\mu}m$ for the histologic examination and analysis. Results: 1. We could achieve the successful reconstruction of the horizontal bone defect by auto block bone graft. The grafted bone block remained stable morohologically after 16 weeks of the surgery. 2. In the histologic view. We observed osteoid tissue from the sample $4^{th}$ week sample and active capillary reconstruction in the grafted bone from the $12^{th}$ week sample. Healing procedures of auto bone grafts were compared to that of the host bone. 3. Bone mineralization could be detected from the $8^{th}$ week sample. 4. Fluorochrome labeling showed active bony changes and formation at the interface of the host bone and the block graft mainly. Bony activation in the grafted bone could be seen from the $4^{th}$ week samples. Conclusions: Active bone formation and remodeling between the grafted bone and host bone can be seen through the revascularization. After the perfect adhesion to host bone, Timing of successful implant installation can be detected through the ideal ridge formation by horizontal ridge augmentation.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1266-1268
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• 1996

In this study an automatic roiling-coli labeling system using robot vision system and peripheral mechanism is proposed and implemented, which instead of the manual labor to attach labels Rolling-coils in a steel miil. The binary image process for the image processing is performed with the threshold, and the contour line is converted to the binary gradient which detects the discontinuous variation of brightness of rolling-coils. The moment invariants algorithm proposed by Hu is used to make it easy to recognize even when the position of the center are different from the trained data. The position error compensation algorithm of six degrees of freedom industrial robot manipulator is also developed and the data of the position of the center rolling-coils, which is obtained by floor mount camera, are transfered by asynchronous communication method. Therefore even if the position of center is changed, robot moves to the position of center and performs the labeling work successfully. Therefore, this system can be improved the safety and efficiency.